Valorizing ‘Omics Visualization for Discovery

Tracking #: 457-1437


Christine ChichesterORCID logo

Responsible editor: 

Tobias Kuhn

Submission Type: 

Position Paper


Scientists from diverse backgrounds are joining the field of data science. This leads to advances in data science being actualized in the context of many different domains. Conclusions from datasets using innovative algorithms are obvious aspects but advances in data science can take on many different forms such as new methods for data interpretation, new data integration and processing technologies, or as will be the topic of this editorial, data visualization techniques. The parity and complementary relationship between techniques from all domains provide ways to improve discovery although quantifying the contributions to discovery process from each technique can be elusive. The experiences described here come from a visualizing life science multi-omics data, but most of the remarks can be associated with visualization methods in general. From the perspective that visualization serves as an important method for shaping data science interpretations, this paper sets out some of the difficulties encountered in creating and valorizing new visualization implementations for scientific discovery from multi-omics datasets.


Supplementary Files (optional): 

Previous Version: 


  • Reviewed

Data repository URLs: 


Date of Submission: 

Friday, April 28, 2017

Date of Decision: 

Thursday, May 11, 2017

Nanopublication URLs:



Solicited Reviews:


Meta-Review by Editor

I agree with the reviewers that the following issues should be addressed:

- Concrete examples with more details should be provided (reviewer 1)

- The logic structure and position should be made explicit (reviewer 2)

- The issue with respect to 3D graph visualizations and hierarchical data should be resolved (reviewer 3)

Tobias Kuhn (